FEUAGame: Fairness-Aware Edge User Allocation for App Vendors

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Jingwen Zhou;Feifei Chen;Guangming Cui;Yong Xiang;Qiang He
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Abstract

Mobile edge computing (MEC) offers a new computing paradigm that turns computing and storage resources to the network edge to provide minimal service latency compared to cloud computing. Many research works have attempted to help app vendors allocate users to appropriate edge servers for high-performance service provisioning. However, existing edge user allocation (EUA) approaches have ignored fairness in users’ data rates caused by interference, which is crucial in service provisioning in the MEC environment. To pursue fairness in EUA, edge users need to be assigned to edge servers so their quality of experience can be ensured at minimum costs without significant service performance differences among them. In this paper, we make the first attempt to address this fair edge user allocation (FEUA) problem. Specifically, we formulate the FEUA problem, prove its $\mathcal {NP}$ -hardness, and propose an optimal approach to solve small-scale FEUA problems. To accommodate large-scale FEUA scenarios, we propose a game-theoretic approach called FEUAGame that transforms the FEUA problem into a potential game that admits a Nash equilibrium. FEUA employs a decentralized algorithm to find the Nash equilibrium in the potential game as the solution to the FEUA problem. A widely-used real-world data set is utilised to experimentally compare the performance of FEUAGame to four representative approaches. The numerical outcomes show the effectiveness and efficiency of the proposed approaches in solving the FEUA problem.
FEUAGame:面向应用程序供应商的公平感知边缘用户分配
与云计算相比,移动边缘计算(MEC)提供了一种新的计算模式,它将计算和存储资源转向网络边缘,以提供最小的服务延迟。许多研究工作都试图帮助应用程序供应商将用户分配到合适的边缘服务器,以提供高性能服务。然而,现有的边缘用户分配(EUA)方法忽略了干扰导致的用户数据速率的公平性,而这在 MEC 环境中的服务供应中至关重要。为了追求 EUA 的公平性,需要将边缘用户分配给边缘服务器,这样就能以最小的成本确保他们的体验质量,而不会造成他们之间明显的服务性能差异。本文首次尝试解决边缘用户公平分配(FEUA)问题。具体来说,我们提出了 FEUA 问题,证明了它的 $\mathcal {NP}$ 硬度,并提出了解决小规模 FEUA 问题的最优方法。为了适应大规模的 FEUA 情景,我们提出了一种名为 FEUAGame 的博弈论方法,它将 FEUA 问题转化为一个潜在博弈,该博弈允许一个纳什均衡。FEUA 采用分散算法在潜在博弈中找到纳什均衡作为 FEUA 问题的解决方案。我们利用广泛使用的现实世界数据集,通过实验比较了 FEUAGame 与四种代表性方法的性能。数值结果显示了所提方法在解决 FEUA 问题方面的有效性和效率。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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